Simplified model for the short-term forecasting of heat loads in buildings

被引：6
|
作者：
Eguizabal, Markel [1 ,2 ]
Garay-Martinez, Roberto [3 ]
Flores-Abascal, Ivan [2 ]
机构：
[1] Geuria Ingn & PRL, Nafarroa Kalea 19, Trapagaran 48510, Spain
[2] Univ Basque Country UPV EHU, Fac Engn Bilbao, Dept Energy Engn, ENEDI Res Grp, Ingeniero Torres Quevedo 1, Bilbao 48013, Spain
[3] Univ Deusto, Fac Engn, Inst Technol, Ave Univ 24, Bilbao 48007, Spain
来源：
ENERGY REPORTS | 2022年 / 8卷
关键词：
Regression model; Heating demand; Building; Lagged value; ENERGY DEMAND; PREDICTION; SYSTEM;
D O I：
10.1016/j.egyr.2022.10.224
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
A data-driven model is used to predict one-hour ahead heat loads based on present and recent history of weather and heat loads. A computationally inexpensive method is built to deliver load forecasting based on existing data quality and resolution from smart meters. Optimal model formulation is discussed and optimized at 4-hour historical values. The model is trained and tested against synthetic data from a building energy simulation, resulting in absolute error <4% and R-2 values in the range of 0.92 to 0.94. (C) 2022 The Author(s). Published by Elsevier Ltd.
引用
收藏
页码：79 / 85
页数：7
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